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library(plotcli)
plot_width = 80
plot_height = 40
# Create some example data
x <- seq(0, 2 * pi, length.out = 100)
y1 <- sin(x)
y2 <- cos(x)
data_1 = list(
x = x,
y = y1,
name = "y1 = sin(x)",
color = "blue",
type = "line",
braille = FALSE
)
data_2 = list(
x = x,
y = y2,
name = "y2 = cos(x)",
color = "red",
type = "line",
braille = FALSE
)
# Create a plotcli object with specified dimensions
plot <- plotcli$new(
plot_width = plot_width,
plot_height = plot_height,
x_label = "X-axis",
y_label = "Y-axis"
)
# Add data sets to the plot
plot$add_data(data_1)
plot$add_data(data_2)
# Print the plot
plot$print_plot()
#>
#>
#> ┌────────────────────────────────────────────────────────────────────────────────┐
#> 1.0 │****** ********** *****│
#> │ * ** ** * │
#> │ ** * * ** │
#> │ * ** * * │
#> │ * * * * │
#> │ ** * ** │
#> │ * * * * │
#> │ * * * * │
#> │ * * * * │
#> │ * * * * │
#> 0.5 │ * * * * │
#> │ * * * * │
#> │ * * * * │
#> │ * * * * │
#> │ * * * * │
#> │ * * * * │
#> │ * * * * │
#> │ * * * * │
#> │ * * * * │ y1 = sin(x)
#> Y-axis 0.0 │* * * * *│ y2 = cos(x)
#> │ * * * *│
#> │ * * * * │
#> │ * * * * │
#> │ * * * * │
#> │ * * * * │
#> │ * * * * │
#> │ * * * * │
#> │ * * * * │
#> │ * * * * │
#> -0.5 │ * * * * │
#> │ * * * * │
#> │ * * * * │
#> │ * * * * │
#> │ * * * │
#> │ * * * * │
#> │ * * * ** │
#> │ * * * * │
#> │ ** ** ** ** │
#> │ * * ** *** │
#> -1.0 │ ******* ****** │
#> └────────────────────────────────────────────────────────────────────────────────┘
#>
#> 0.0 1.6 3.1 4.7 6.3
#>
#> X-axis
#> plotcli can use braille characters for plotting scatter and line plots, which give a much higher resolution on supported terminals. Whether to use braille or ascii characters can be specified for each data set.
library(plotcli)
data(mtcars)
# Fit a linear model
lm_fit <- lm(wt ~ mpg, data = mtcars)
# Create a new dataset with the predicted values
predicted_data <- data.frame(mpg = mtcars$mpg, predicted_wt = predict(lm_fit, mtcars))
# we use braille characters for the regression line
data_1 = list(
x = predicted_data$mpg,
y = predicted_data$predicted_wt,
name = "Regression Line",
color = "red",
type = "line",
braille = TRUE
)
# and ascii characters for the raw data
data_2 = list(
x = mtcars$mpg,
y = mtcars$wt,
name = "Data Points",
color = "blue",
type = "scatter",
braille = FALSE
)
# Create a plotcli object
plot_width = 80
plot_height = 40
plot_obj <- plotcli$new(
plot_width,
plot_height,
x_label = "Miles per Gallon",
y_label = "Weight"
)
# Add raw data and regression line
plot_obj$add_data(data_1)
plot_obj$add_data(data_2)
# Print the plot
plot_obj$print_plot()
#>
#>
#> ┌────────────────────────────────────────────────────────────────────────────────┐
#> 5.4 │* * │
#> │* │
#> │ │
#> │ │
#> │ │
#> │ │
#> │ │
#> │⡢ │
#> │ ⠡⠡ │
#> │ ⠡⠡⠡ │
#> 4.4 │ ⠡⠡ │
#> │ ⠡⡂⡁ │
#> │ ⠁⡂⡂ * │
#> │ ⡓⡕⠢ │
#> │ * ⠖⠻⠵⠐ * │
#> │ *⠔⡈⡆⠲ * │
#> │ ⠒⠷⡙⠳ │
#> │ * ** ⠐⠶⡙⡇⠢ │
#> │ * ⠐⡀**⠥* * │ Regression Line
#> Weight 3.3 │ ⡃⡪⢩⡄ │ Data Points
#> │ ⠥⡩⡆ * │
#> │ * ⠠⡦⡆⠦ * * │
#> │ ⠠⡠⡦⡦ │
#> │ ⠐⠲⢅⡳⠣ │
#> │ ** ⠒⡢⢅⠥⠡ │
#> │ * ⠂⠒⠖⠥⠥ │
#> │ * ⠒⠒⠷⠥⠠ │
#> │ ⠒⠔⠢⠢ │
#> │ * ⠒⠒⠴⠢⠠ │
#> 2.3 │ * ⠤⠦⠦⠢ │
#> │ ⠠⠤⠦⠤ * │
#> │ * ⠠⠢⠤⠄⠂ │
#> │ ⠠⠢⠄⠂ │
#> │ * ⠠⠢⠤⠄⠂ │
#> │ ⠠⠢⠄⠄ *│
#> │ ⠠⡐⠴⠄⠂ │
#> │ *⠰⠲⠴⠤ │
#> │ * ⠐⠴⠴⠂ │
#> │ ⠐⠒⠒ │
#> 1.3 │ ⠒⠒│
#> └────────────────────────────────────────────────────────────────────────────────┘
#>
#> 10.4 16.3 22.1 28.0 33.9
#>
#> Miles per Gallon
#> These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.